A schematic approach to diagnosing and resolving lecturalgia
Bibliographic record
Abstract
BACKGROUND: The lecture is a much used and much criticized teaching method. Lecturalgia (painful lecture) is a frequent cause of morbidity for both teachers and learners. The etiology of lecturalgia is multifactorial and multiple lecturing pathologies frequently coexist. The 'Clinical Presentation' curriculum at the University of Calgary encourages the use of 'schemes' that provide a scaffolding for learning and a starting point for approaching (clinical) problems. Thus far this approach has not been used to tackle teaching or learning problems. AIM: Our aim in this paper was to devise a schematic approach to diagnosing lecturing problems and to make evidence-based recommendations on how to resolve lecturing problems. We have suggested that causes of lecturalgia can be divided into three categories: poor judgement; poor organization; and poor delivery. Our proposed scheme is based upon these three categories that are then subcategorized. RESULTS: We have reviewed the medical education literature in an attempt to provide evidence-based recommendations for the remediation of lecturing problems within each subcategory. CONCLUSION: Where trial evidence is lacking we have made recommendations that are consistent with cognitive theory or expert opinion. Finally, where expert opinion does not exist, we have taken the liberty (literary license) of providing nonexpert opinion!
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".